Project Summary This interdisciplinary study will translate, disseminate, implement, and adopt science-based recommendations and guidelines for the care of acutely injured crash victims. The William Lehman Injury Research Center developed the current standard for crash injury risk prediction models. It is a goal of this proposal to refine and evaluate these methods using vehicle telematics data to immediately identify motor vehicle occupants who may be seriously injured following a motor vehicle crash. Current triage criteria are inaccurate and occur only after arrival at the crash scene. This study is timely as many vehicles are now equipped with Advanced Automatic Crash Notification (AACN) technology that automatically transmits valuable location and crash information that can be used to predict the risk of injury to the occupants. In 2007, the CDC formed a Committee that drafted a revised triage protocol recommending the use of an injury risk prediction algorithm using AACN data and the need for a pilot evaluation using this enhanced triage criteria. This proposal will develop and evaluate strategies to interpret crash data and assess how such data improves emergency care and saves lives. The study will enhance injury risk models using AACN parameters currently collected by over 4 million vehicles, and eventually all vehicles. Training of the Injury Prediction Model will use historical data from the National Highway Traffic Safety Administration database. Real world data transmitted by AACN vehicles will then be analyzed to compare estimated injury risks with the level of medical care required, performed over a three-year period in 5 US States where AACN equipped vehicles are present in large numbers. Our overall hypothesis is that models and systems using AACN data can be enhanced and implemented to improve the triage and outcome of acutely injured crash victims. This proposal will develop and evaluate the success and implementation of this new triage paradigm. In support of the approach to examine successful models and the evaluation of guidelines in acute injury care, Specific Aim 1 seeks to establish a model which best characterizes injury risk based on data elements currently available and transmitted remotely by vehicles with AACN systems, Specific Aim 2 seeks to evaluate the proposed injury model by comparing crash severity rating derived from telematics data with a crash involved occupant's need for hospital or trauma center transport by prospectively analyzing real-world crashes of vehicles with AACN technology. Specific Aim 3 seeks to design a pilot implementation for dispatch in Miami, Florida utilizing telematics data to improve dispatch protocols. This study will lead to substantial improvements in both the time to first responder arrival and enhanced decision- making capabilities using AACN data. The ability to reduce transport time and improve the accuracy of trauma triage using a decision model based on real-time crash data will represent a substantial improvement over current dispatch and triage protocols. The savings in costs to the healthcare system, the potential improvements in patient outcome, and the added overall safety of the transportation system will be significant. PUBLIC HEALTH RELEVANCE: Project Narrative Today, 911 emergency medical systems rely on subjective and often unreliable information and do not take advantage of newer vehicle sensor technologies, leading to inefficient and suboptimal care of the injured crash victim. Utilization of this technology will lead to substantial improvements in both the time to first responder arrival and the accuracy of trauma triage and medical decision-making. The savings in costs to the healthcare system, the potential improvements in patient outcome, and the added overall safety of the transportation system will be significant.